Automated Document Analysis UI for Content Type Error Detection
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Solution Overview
Problem
Standard spelling and grammar technologies struggle to identify errors in names, acronyms, and intentionally misspelled company or product names due to their absence in standard dictionaries, leading to difficulties in catching mistakes in automated document analysis.
Innovation Solution
Automated document analysis systems generate a user interface that identifies and groups occurrences of specific content types, such as names, within a document, arranging them in alphanumeric order and providing indicia to highlight potential errors, allowing for improved error recognition and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard spelling and grammar technologies are used to analyze documents, then processing speed and automation are improved, but the ability to identify errors in names, acronyms, and intentionally misspelled terms deteriorates
Solution Approach 1:
The system segments the document analysis task into different content type categories (names, acronyms, dates, locations, etc.) and applies specialized detection methods to each segment. This allows standard spell checkers to handle common words while specialized algorithms handle names and acronyms separately, resolving the contradiction between fast processing and accurate error detection for specialized terms.
Solution Approach 2:
The system changes the detection parameters dynamically based on content type. For names and acronyms, it uses different detection parameters (such as pattern matching, context analysis, and comparison with known entities) rather than standard spelling dictionaries. This parameter adaptation enables reliable error detection in specialized terms while maintaining overall processing efficiency.
2Measurement precision
If standard spelling dictionaries are used for error detection, then common spelling errors are identified accurately, but names, acronyms, and made-up words are missed
Solution Approach 1:
The system creates a multi-functional error detection approach that handles multiple types of content (common words, names, acronyms, made-up words) using a unified platform. By integrating content type classification with specialized detection algorithms for each type, the system achieves both accurate common spelling detection and adaptability to specialized terms without requiring separate systems.
Solution Approach 2:
The system introduces an intermediary layer of content type classification that mediates between the text analysis and error detection processes. This intermediary categorizes each word or phrase by its content type and routes it to the appropriate detection algorithm, enabling accurate handling of specialized terms while maintaining the overall structure of standard spell checking.
3Loss of information
If all text occurrences are displayed in the user interface, then complete information is provided, but the interface complexity and difficulty of reviewing increases
Solution Approach 1:
The user interface segments the displayed information by content type, organizing occurrences of names, acronyms, dates, and other elements into separate sections or groups. This segmentation reduces the cognitive load on users by presenting related information together rather than as a flat list, making the interface more manageable while preserving complete information.
Solution Approach 2:
The system applies different display qualities and formats to different content types within the user interface. For example, names might be highlighted differently from dates, or occurrences of the same content type might be grouped together with additional context. This local differentiation makes the interface more intuitive and easier to review while maintaining information completeness.
Data Source
AI summary
At least one processing device, operating upon a body of text in a document, identifies occurrences of at least one content type in the body of text. The at least one processing device thereafter generates a user interface that includes portions of text from the body of text that are representative of at least some of the occurrences of the at least one content type in the document. For each content type, the occurrences corresponding to that content type can be grouped together to provide grouped content type occurrences that are subsequently collocated in the user interface. Those portions of text corresponding to the grouped content type occurrences may be arranged in alphanumeric order. The user interface may comprise at least a portion of the body of text as well as indicia indicating instances of the occurrences within the portion of the body of text.